Lectures related to Probit model

MLE Applications: Binary Choice Models

Explores the application of Maximum Likelihood Estimation in binary choice models, covering probit and logit models, latent variable representation, and specification tests.

Maximum Likelihood Estimation: Theory

Covers the theory behind Maximum Likelihood Estimation, discussing properties and applications in binary choice and ordered multiresponse models.

Logistic Regression: Interpretation & Feature Engineering

Covers logistic regression, probabilistic interpretation, and feature engineering techniques.

Nonparametric Regression: Kernel-Based Estimation

Covers nonparametric regression using kernel-based estimation techniques to model complex relationships between variables.

Mixtures: introduction

Introduces mixtures, covers discrete and continuous mixtures, explores examples, and discusses combining probit and logit models.

Binary Responses: Link Functions and GLMs

Explores link functions for binary responses and the impact of sparseness on model interpretability.

Binary Response: Link Functions

Explores binary response interpretation, link functions, logistic regression, and model selection using deviances and information criteria.

Binary Choice Models and Time Series Analysis

Explores binary choice models like probit and logit, as well as univariate time series analysis with ARIMA models for forecasting economic variables.

ARCH and GARCH Models: Volatility Forecasting

Covers ARCH and GARCH models for volatility forecasting in risk-factor changes.

Statistical Inference for Bandit Data

Explores statistical inference for bandit data, focusing on personalized treatment actions and challenges of standard estimators.

Long Memory and ARCH: Time Series

Explores long memory in time series and ARCH models for financial volatility.

Multilevel Models: Understanding Nested Data Structures

Delves into multilevel models, emphasizing nested data structures and intra-class correlation, and explores random-intercept and random-slope models.

Maximum Likelihood Theory & Applications

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Variational Inference and Neural Networks

Covers variational inference and neural networks for classification tasks.

Assumption-lean Inference: Generalised Linear Model Parameters

Explores assumption-lean inference for statistical estimands in generalised linear models, emphasizing robust and generic approaches.

Logistic Regression: Cost Functions & Optimization

Explores logistic regression, cost functions, gradient descent, and probability modeling using the logistic sigmoid function.